StochSimR - Stochastic Process Simulation Engine

A modular simulation engine for stochastic processes. Provides exact and approximate simulation methods for Poisson processes, Brownian motion, discrete-time Markov chains, Levy processes (gamma, normal inverse Gaussian, variance-gamma, alpha-stable), Merton jump-diffusion models, Hawkes self-exciting processes, geometric Brownian motion, and Ornstein-Uhlenbeck mean-reverting diffusions. Includes variance reduction techniques (antithetic variates, control variates, importance sampling, stratified sampling), parallel simulation via the 'future' framework, rare-event simulation (cross-entropy and multilevel splitting), path visualization, and summary statistics. Methods are based on Glasserman (2003) <doi:10.1007/978-0-387-21617-1> and Asmussen & Glynn (2007) <doi:10.1007/978-0-387-69033-9>.